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The Indomitable Magnetic Resonance Imaging System is indicated for use in producing images of multiple planes in the head and body. These images correspond to the distribution of hydrogen nuclei exhibiting nuclear magnetic resonance (NMR) and depend for their contrast upon NMR parameters [hydrogen nuclei concentration and flow velocity, T1 (spinlattice relaxation time) and T2 (spin-spin relaxation time)]. As a result of the acquisition and processing of the NMR data, these images display the internal structure of the head and body, and when interpreted by a trained physician, can vield diagnostically useful information.

FONAR Corporation has modified the existing control limits for the Indomitable scanner gradient subsystem to allow the maximum gradient current to increase by 50 percent, with a corresponding increase in the maximum slew rate for the gradient waveforms. All other subsystems remain essentially unchanged from their previously approved (K002490) state.

This modified gradient subsystem follows the same basic design principles as our previously approved gradient systems. It functions on the same basic operating and physical principles as the predicate device, the Indomitable gradient system (K002490). It is constructed with the same manufacturing materials, and is fabricated in the same fashion to the previously approved gradients.

The primary differences of this modification to the gradient subsystem are the software limits for maximum gradient current value and the increased slew rate that accompanies the changed current value. It is, in summary, a significant equivalent of FONAR's currently approved predicate device.

The provided text describes a special 510(k) submission for a device modification, specifically an increase in the operational limits for the gradient subsystem of an MRI scanner. The goal of the study was to demonstrate that this modification does not adversely affect patient safety and that the device remains substantially equivalent to its predicate device. This is primarily a safety validation rather than a diagnostic performance study in the typical sense of AI-powered diagnostic devices.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device modification are primarily focused on maintaining patient safety and demonstrating substantial equivalence to the predicate device, particularly regarding the absence of Peripheral Nerve Stimulation (PNS) at the new operational limits. The "reported device performance" here refers to the outcomes of the safety testing.

Study Details

This submission focuses on a safety validation for a hardware/software modification, not a diagnostic AI algorithm. Therefore, many standard AI/diagnostic study metrics (like specific test set sizes for diagnostic accuracy, expert qualifications for ground truth in diagnostic tasks, MRMC studies, or training set details) are not directly applicable or reported in the same way.

1. Sample size used for the test set and the data provenance:

   • Sample Size: This is a clinical trial involving "volunteers," implying a relatively small number of healthy individuals rather than a large patient cohort for diagnostic validation. The exact number of volunteers is not specified in the provided text, only that "All volunteers completed the test."
   • Data Provenance: Prospective. The study was a "voluntary clinical trial" specifically conducted for this submission. The origin of the volunteers (e.g., country) is not specified but is implicitly within the context of FDA regulations in the US.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Ground Truth Establishment: For this safety study, the "ground truth" is the absence of PNS. This was established directly by the test subjects themselves reporting any "sensations associated with PNS." There isn't a need for multiple external experts to establish a "ground truth" in the diagnostic sense, as the criterion is subjective patient experience combined with adherence to international safety standards.
   • Expert Qualifications: The study was implicitly supervised by medical professionals (as required for a clinical trial), but no specific number or qualifications of experts involved in establishing ground truth (i.e., PNS assessment beyond patient self-reporting) are detailed.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

   • Adjudication Method: Not applicable in the context of this safety study. The primary "adjudication" for PNS was the direct report from the "volunteers" themselves according to the IEC 60601-2-33 standard. There is no mention of independent reviewers or a consensus process for assessing PNS beyond this.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   • MRMC Study: No. This is not an AI-assisted diagnostic device; it's a modification to an MRI scanner's gradient system. Therefore, an MRMC study and effects on human reader performance are not applicable.

5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

   • Standalone Performance: Not applicable. This device is an MRI scanner modification, not a standalone algorithm. The "algorithm" here refers to the system's operating software, which sets hardware limits. The performance evaluated is the physical system's safety, not an algorithm's diagnostic output.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • Ground Truth Type: Patient self-reporting of physiological sensation (absence of PNS) per the guidelines of international safety standards (IEC 60601-2-33). This is complemented by non-clinical technical measurements to confirm adherence to new physical operating parameters.

7. The sample size for the training set:

   • Training Set Sample Size: Not applicable. This is not a machine learning or AI algorithm in the context of a "training set" for diagnostic performance.

8. How the ground truth for the training set was established:

   • Training Set Ground Truth: Not applicable, as there is no training set mentioned for this type of device modification.

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